{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import re\n",
    "import nltk\n",
    "import spacy\n",
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.626151\n",
       "0    0.373849\n",
       "Name: IsUnderRisk, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load the train data\n",
    "train = pd.read_csv(r\"D:\\finance_default\\Train.csv\")\n",
    "train.IsUnderRisk.value_counts([0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = train.IsUnderRisk\n",
    "X = train.drop([\"IsUnderRisk\"],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Location_Score</th>\n",
       "      <th>Internal_Audit_Score</th>\n",
       "      <th>External_Audit_Score</th>\n",
       "      <th>Fin_Score</th>\n",
       "      <th>Loss_score</th>\n",
       "      <th>Past_Results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>8.032</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31</td>\n",
       "      <td>77.730</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>59.203</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>73.080</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>15.666</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   City  Location_Score  Internal_Audit_Score  External_Audit_Score  \\\n",
       "0     2           8.032                    14                     8   \n",
       "1    31          77.730                     8                     3   \n",
       "2    40          59.203                     3                    12   \n",
       "3    12          73.080                     4                     5   \n",
       "4     4          15.666                    13                    15   \n",
       "\n",
       "   Fin_Score  Loss_score  Past_Results  \n",
       "0          3           6             0  \n",
       "1          3           8             1  \n",
       "2         11           3             0  \n",
       "3          7           6             0  \n",
       "4          6           7             2  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 543 entries, 0 to 542\n",
      "Data columns (total 7 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   City                  543 non-null    int64  \n",
      " 1   Location_Score        543 non-null    float64\n",
      " 2   Internal_Audit_Score  543 non-null    int64  \n",
      " 3   External_Audit_Score  543 non-null    int64  \n",
      " 4   Fin_Score             543 non-null    int64  \n",
      " 5   Loss_score            543 non-null    int64  \n",
      " 6   Past_Results          543 non-null    int64  \n",
      "dtypes: float64(1), int64(6)\n",
      "memory usage: 29.8 KB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Location_Score</th>\n",
       "      <th>Internal_Audit_Score</th>\n",
       "      <th>External_Audit_Score</th>\n",
       "      <th>Fin_Score</th>\n",
       "      <th>Loss_score</th>\n",
       "      <th>Past_Results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>543.000000</td>\n",
       "      <td>543.000000</td>\n",
       "      <td>543.000000</td>\n",
       "      <td>543.000000</td>\n",
       "      <td>543.000000</td>\n",
       "      <td>543.000000</td>\n",
       "      <td>543.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.576427</td>\n",
       "      <td>32.259346</td>\n",
       "      <td>8.189687</td>\n",
       "      <td>7.327808</td>\n",
       "      <td>7.046041</td>\n",
       "      <td>5.530387</td>\n",
       "      <td>0.613260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.722687</td>\n",
       "      <td>24.887291</td>\n",
       "      <td>3.312022</td>\n",
       "      <td>3.450667</td>\n",
       "      <td>3.156978</td>\n",
       "      <td>1.839124</td>\n",
       "      <td>0.764509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.185000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.565000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>19.803000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>62.594000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>80.809000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             City  Location_Score  Internal_Audit_Score  External_Audit_Score  \\\n",
       "count  543.000000      543.000000            543.000000            543.000000   \n",
       "mean    19.576427       32.259346              8.189687              7.327808   \n",
       "std     14.722687       24.887291              3.312022              3.450667   \n",
       "min      0.000000        5.185000              3.000000              3.000000   \n",
       "25%      6.000000       13.565000              6.000000              5.000000   \n",
       "50%     14.000000       19.803000              8.000000              7.000000   \n",
       "75%     37.000000       62.594000             11.000000             10.000000   \n",
       "max     44.000000       80.809000             15.000000             15.000000   \n",
       "\n",
       "        Fin_Score  Loss_score  Past_Results  \n",
       "count  543.000000  543.000000    543.000000  \n",
       "mean     7.046041    5.530387      0.613260  \n",
       "std      3.156978    1.839124      0.764509  \n",
       "min      3.000000    3.000000      0.000000  \n",
       "25%      5.000000    4.000000      0.000000  \n",
       "50%      7.000000    6.000000      1.000000  \n",
       "75%      8.000000    7.000000      1.000000  \n",
       "max     15.000000   13.000000     10.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "corr = X.corr()\n",
    "sn.heatmap(corr, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000211BB1ED160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000211BB218710>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000211BB2489B0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000211BB278C50>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.boxplot(by ='IsUnderRisk', column =['Internal_Audit_Score','External_Audit_Score',\n",
    "                                          'Fin_Score','Loss_score'], grid = False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x211bb42f9e8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = train.loc[train['IsUnderRisk']==1,]\n",
    "c = train.loc[train['IsUnderRisk']==0,]\n",
    "plt.scatter(b['Location_Score'],b['Fin_Score'])\n",
    "plt.scatter(c['Location_Score'],c['Fin_Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x211bb0d6a90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(b['Location_Score'],b['Internal_Audit_Score'])\n",
    "plt.scatter(c['Location_Score'],c['Internal_Audit_Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x211bb0606a0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(b['Location_Score'],b['External_Audit_Score'])\n",
    "plt.scatter(c['Location_Score'],c['External_Audit_Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x211bb159668>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(b['Location_Score'],b['Loss_score'])\n",
    "plt.scatter(c['Location_Score'],c['Loss_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x211bb4c31d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(b['Location_Score'],b['Past_Results'])\n",
    "plt.scatter(c['Location_Score'],c['Past_Results'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x211bb531160>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAecElEQVR4nO3dbWxc5ZkG4PvxOHaCEycksQMkQMBC5EfihcgoTqO2UamViFJI+6MLSiRKu9D9A6tF0JYa4TEibaVUtIVK3eWj0BVRqgrR0K5oqMWKbYXiCPPRhF3SjcxHSEhsJyFx4iZ2bD/7w3PG4+MZxnPmnWfmPbkvyQpz4vKcd2Z892Af36+oKoiIyD9V5T4BIiKKhgFOROQpBjgRkacY4EREnmKAExF5qtpy2OLFi3X58uWWI4mIvPfmm28eU9WG8HHTAF++fDl6enosRxIReU9EPsp2nN9CISLyFAOciMhTDHAiIk8xwImIPMUAJyLylOldKERRtD32Gg70D6UfX9NYh6771pfvhCKyWMeK9pdxbmyyoG52QrB/601OZ+x8+zC2vfI3fHLyLC5bMAcPbLgWm65f6nTGmq1d6Ds9kn68ZF4N9rS3OZ1h4aGd+7Bjz8cYU0VCBLevuRyPblrl7N/PK3CqaOHQA4AD/UNoe+y18pxQRBbrCIc3AJwbU6xof9nZjJ1vH8aDL+7D4ZNnoQAOnzyLB1/ch51vH3Y2IxzeANB3egRrtnY5m2HhoZ378Hz3QYylGl/HVPF890E8tHOfsxkMcKpo4dDLd7xSWawjHN75jkex7ZW/4ez5sSnHzp4fw7ZX/uZsRji88x2vVDv2fFzQ8SgY4EQ0Y5+cPFvQ8QvZWI69FnIdj4IBTkQzdtmCOQUdv5AlRAo6HgUDnCraNY11BR2vVBbrmJ3IHgy5jkfxwIZrMWdWYsqxObMSeGDDtc5mLJlXU9DxSnX7mssLOh4FA5wqWtd966eFnI93oVisY//Wm6aFteu7UDZdvxQ/+voqLF0wBwJg6YI5+NHXVzm9C2VPe9u0sPbxLpRHN63CltYr0lfcCRFsab3C6V0oYrknZktLi7LMioioMCLypqq2hI/zCpyIyFMMcCIiTzHAiYg8xQAnIvIUA5yIyFMMcCIiTzHAiYg8xQAnIvIUA5yIyFN5N3QQkV8BuBlAv6quDP3d/QC2AWhQ1WOlOUVbFmX1FuKyjjix2KRg81O78XrvifTjdU0Lsf2utU5nlHqTAqsZFl8jpX49ZnIF/hyAjeGDInI5gDYAB52dTZlZlNVbiMs64sRik4JwWADA670nsPmp3c5mWGxSYDHD4mvE4vXIG+Cq+mcAJ7L81U8BfBeAXZlKiVmU1VuIyzrixGKTgnBY5DsehcUmBRYzLL5GLF6PSN8DF5FbABxW1b/O4HPvFpEeEekZGBiIMs5MXMrq47IOqjwWmxRYzIjL10jBAS4iFwFoB/DwTD5fVZ9U1RZVbWloaCh0nKm4lNXHZR1UeSw2KbCYEZevkShX4E0ArgLwVxH5EMAyAG+JyCUuT6wcLMrqLcRlHXFisUnBuqaFBR2PwmKTAosZFl8jFq9HwQGuqvtUtVFVl6vqcgCHAKxW1aPOzqpMLMrqLcRlHXFisUnB9rvWTgsH13c9WGxSYDHD4mvE4vXIu6GDiOwAsB7AYgB9ADpU9ZmMv/8QQMtMbiPkhg5ERIXLtaFD3vvAVfX2PH+/vIjzIiKiiPibmEREnmKAExF5igFOROQpBjgRkacY4EREnmKAExF5igFOROSpvPeBE32WuPSOW/RPW/SBN3fswuDwZMtefW0CezuntUEXpe2x13Cgfyj9+JrGOnTdt97pDAsXSh84UVZx6R236J+26AMPhzcADA6Pobljl7MZ4fAGgAP9Q2h77DVnMyxcMH3gRLnEpXfcon/aog88HN75jkcRDu98xyvVBd0HTgTEp1PZon+aKktc3rsMcIosLp3KFv3TVFni8t5lgFNkcekdt+iftugDr69NFHQ8imsa6wo6Xqku2D5wokBcesct+qct+sD3dm6cFtau70Lpum/9tLD28S6UC6YP3CX2gRMRFS5XHzivwImIPMUAJyLyFAOciMhTDHAiIk8xwImIPMUAJyLyFAOciMhTDHAiIk/l7QMXkV8BuBlAv6quTB3bBuCrAEYA9AK4U1VPlvJEAZvOZiqMRaeyxetusQ6LGRZd3Ra95nxfzcxMrsCfAxD+XdwuACtVtRnA/wF40NkZ5WDR2UyFsehUtnjdLdZhMcOiq9ui15zvq5nLG+Cq+mcAJ0LH/qSqo6mH3QCWOTujHCw6m6kwFp3KFq+7xTosZlh0dVv0mvN9NXMuvgf+LQB/zPWXInK3iPSISM/AwEDkIexsrjwWncoWr7vFOuLSP22B76uZKyrARaQdwCiA7bk+R1WfVNUWVW1paGiIPIudzZXHolPZ4nW3WEdc+qct8H01c5EDXETuwMQPNzerQaWhRWczFcaiU9nidbdYh8UMi65ui15zvq9mLlKAi8hGAN8DcIuq/t3Z2XwGi85mKoxFp7LF626xDosZFl3dFr3mfF/NXN4+cBHZAWA9gMUA+gB0YOKuk1oAx1Of1q2q/5xvGPvAiYgKl6sPPO994Kp6e5bDzzg5KyIiioy/iUlE5CkGOBGRpxjgRESeYoATEXmKAU5E5CkGOBGRpxjgRESeynsfeCWJS2dzXLqOreZYPF8Wmjt2YXB4sp2uvjaBvZ3hpubiWPSBb35qN17vnSwoXde0ENvvWuvdDIv3VanX4c0VeFw6m+PSdWw1x+L5shAObwAYHB5Dc8cuZzMs+sDDgQQAr/eewOandns1w+J9ZbEObwI8Lp3Ncek6tppj8XxZCId3vuNRWPSBhwMp3/FKnWHxvrJYhzcBHpfO5rh0HVvNYY82lUJc3lfeBHhcOpvj0nVsNYc92lQKcXlfeRPgcelsjkvXsdUci+fLQn1toqDjUVj0ga9rWljQ8UqdYfG+sliHNwEel87muHQdW82xeL4s7O3cOC2sXd+FYtEHvv2utdMCyPWdFRYzLN5XFuvI2wfuEvvAiYgKl6sP3JsrcCIimooBTkTkKQY4EZGnGOBERJ5igBMReYoBTkTkKQY4EZGnGOBERJ7KG+Ai8isR6ReRdzOOLRSRLhE5kPrz4tKeJhERhc1kQ4fnAPwCwH9kHPs+gFdV9cci8v3U4++5P714issGBUB8NnSwmGGx2cKarV3oOz2SfrxkXg32tLd5N2NF+8s4Nzb5W+KzE4L9W29yOsNi04hSb+KR9wpcVf8MIFxgeyuAX6f++dcANjk7o5iLywYFQHw2dLCYYbHZQjhYAaDv9AjWbO3yakY4vAHg3JhiRfvLzmZYbLZgsYlH1O+BL1HVIwCQ+rPR2RnFXFw2KADis6GDxQyLzRbCwZrveKXOCId3vuNRWGy2YLGJR8l/iCkid4tIj4j0DAwMlHpcxYtLkTwQnw0d4vSa0IUlaoD3icilAJD6sz/XJ6rqk6raoqotDQ0NEcfFR1yK5IH4bOgQp9eELixRA/z3AO5I/fMdAF5yczrxF5cNCoD4bOhgMcNis4Ul82oKOl6pM2Ynsl8A5DoehcVmCxabeMzkNsIdAHYDuFZEDonItwH8GECbiBwA0JZ6TDMQlw0KgPhs6GAxw2KzhT3tbdOC1PUdIhYz9m+9aVpYu74LxWKzBYtNPLihAxFRheOGDkREMcMAJyLyFAOciMhTDHAiIk8xwImIPMUAJyLyFAOciMhTDHAiIk/NpA+cKKc4dZuXWlx6zS064OOi1K8HA5wiC3q0gyrWoEcbAEM8xOK5spgRdMAHgg54AAzxEIvXg99Cocji1G1eanHpNbfogI8Li9eDAU6RsUd75uLSa27RAR8XFq8HA5wiY4/2zMWl19yiAz4uLF4PBjhFFqdu81KLS6+5RQd8XFi8HvwhJkUW/CCGd6HkZ/FcWcwIflDJu1Dys3g92AdORFTh2AdORBQzDHAiIk8xwImIPMUAJyLyFAOciMhTDHAiIk8xwImIPMUAJyLyVFG/iSki/wrgnwAogH0A7lTVcy5OLJveZ7+DKz/6LRI6jjGpwkdXfgNNd/67dzPOJRtRq8Ppx8NSi9nJfqcz3uj8PFrG96Yf91Q144aOvzidAQCHH74Sl8nJ9ONPdAGWPvKR0xknHr4EF8tkAdCnOgcLHznqdIZFx/WK9pdxbmzyF+dmJwT7t97kdIbFOuLSAR+HfvbIV+AishTAvQBaVHUlgASA21ydWFjvs9/B1R/+BtUYhwhQjXFc/eFv0Pvsd7yaEYS3CNIftTqMc8lGZzOC8M6c0TK+F290ft7ZDGAyvDPnXCYncfjhK53NCMI7c8bFchYnHr7E2Yyg4zpo1As6rh/auc/ZjHB4A8C5McWK9pedzbBYR9BxffjkWSgmO653vn3Y2QwLFuuwmFHst1CqAcwRkWoAFwH4pPhTyu7Kj36LcOGZyMRxn2YE4R2ekXlFXqwgvMMzMq/IXQjCOzwn84q8WEF4h2dkXpEXy6LjOhze+Y5HYbGOuHTAx6WfPXKAq+phAD8BcBDAEQCnVPVP4c8TkbtFpEdEegYGBiKfaELHCzpeqTOo8sSl49piHXHpgI9LP3sx30K5GMCtAK4CcBmAOhHZEv48VX1SVVtUtaWhoSHyiY5J9lPNdbxSZ1DliUvHtcU64tIBH5d+9mKS6csAPlDVAVU9D+BFAJ9zc1rTfXTlNxC+kFCdOO7TjGGpzTpjWGqdzeipas46o6eq2dkMYOIHltnmfKILnM34VOdknfGpuvsisOi4np3IHqK5jkdhsY64dMDHpZ+9mAA/CKBVRC4SEQFwI4D33JzWdE13/jveX34bRlEFVWAUVXh/+W1O7xCxmDE72Z8O8eDD9V0oN3T8JR3iwUcp7kJZ+shH6RAPPlzfhbLwkaPpEA8+XN+F8uimVdjSekX6SjUhgi2tVzi9e2P/1pumhbXru1As1rHp+qX40ddXYemCORAASxfMwY++vsq7u1As1mExo6g+cBHpBPCPAEYBvA3gn1Rz/zSOfeBERIXL1Qde1H3gqtoBoKOYfwcREUXDn84REXmKAU5E5CkGOBGRpxjgRESeYoATEXmKAU5E5CkGOBGRp4q6D9yaRY/2+c5GVOvwRMO5AKNSi1kd/s2weK4AoDe5ElfrZNvd+3I5mpLvOp1x5IfX4ZLhD9KPj9ZehUt/8I7TGRY2P7Ubr/eeSD9e17QQ2+9a63RGc8cuDA5PNuDV1yawt3Oj0xkW61iztQt9p0fSj5fMq8Ge9janMyxUbB+4NYse7fOdjageH4YgNQNA9fgwznf6NcPiuQImwztzztX6MXqTK53NCMI7c8Ylwx/gyA+vczbDQjj0AOD13hPY/NRuZzPC4Q0Ag8NjaO7Y5WyGxTrC4Q0AfadHsGZrl7MZFnzoAzdj0aNdnWNGtWczLJ4rAOnwDs/JvCIvVhDe4RmZV+Q+CIdevuNRhMM73/EoLNYRDu98xytVRfeBx1KuWhiXtdAWM4io7Cq6DzyWcjV7uqyFtphBRGVX6X3gpix6tEdzzBj1bIbFcwVM/MAy25z3xV3/9NHaq7LOOFp7lbMZFtY1LSzoeBT1tYmCjkdhsY4l82oKOl6pKr0P3JRFj/asjn6MVtVCkZoBYLTK7R0iFjMsnisAaEq+mw7x4MP1XSiX/uCddIgHHz7ehbL9rrXTQs713Rt7OzdOC2vXd6FYrGNPe9u0sPbxLpSK7wMvFPvAiYgKl6sP3JsrcCIimooBTkTkKQY4EZGnGOBERJ5igBMReYoBTkTkKQY4EZGnGOBERJ4qqg9cRBYAeBrASkz8UuG3VNVdr2TIqW2rUT/Um+7RHqxrwvwH3nI6w6Kr++/JBszRyWa1s1KDi5IDTmec6VyKuvEz6cdDVXMxt8NdjWXgWPIqLNLJJrrjshCLk26bAvuSV6ExY0a/LMQSxzNK3dsM2PRoW7B4rh7auQ879nyMMVUkRHD7msvx6KZVTmdYKPU6ir0C/zmAXaq6AsA/AHiv+FPK7tS21ag/0zulR7v+TC9ObVvtbIZFV3cQ3pn91nN0BH9PNjibEYR35oy68TM40+n2iywI78w5i/QEjiXd9ZQE4Z05o1FPoM/hDIveZosebQsWz9VDO/fh+e6DGEv9lviYKp7vPoiHdu5zNsOCxToiB7iI1AP4AoBnAEBVR1T1pKsTC6sf6s3aC10/1OtshkVXdxDe4RmZV+TFCsI7PCPzityFILzDczKvyIvVmGNGo8MZFr3NFj3aFiyeqx17svfJ5zpeqSzWUcwV+NUABgA8KyJvi8jTIlIX/iQRuVtEekSkZ2CgiG8TsKubSsSitzkuLJ6rsRz9TLmOVyqLdRQT4NUAVgP4papeD2AIwPfDn6SqT6pqi6q2NDQU8W0CdnVTiVj0NseFxXOVCP8nV57jlcpiHcUE+CEAh1R1T+rxC5gI9JIYrGvK2gs9WNfkbIZFV/dZqck646y46zoeqpqbdcZQ1VxnM4CJH1hmm3Nc3HVD9+eY0e9whkVvs0WPtgWL5+r2Ndn75HMdr1QW64gc4Kp6FMDHIhK8cjcC+F8nZ5XF/AfewuDcpik92oNz3d6FYtHVfVFyIB3iwYfru1DmdhxOh3jwUYq7UBYnP0iHePDh+i6UJckP0iEefLi+C8Wit9miR9uCxXP16KZV2NJ6RfpKNSGCLa1XeHcXisU6iuoDF5HrMHEbYQ2A9wHcqaqf5vp89oETERUuVx94UfeBq+o7AKb9S4mIqPT4m5hERJ5igBMReYoBTkTkKQY4EZGnGOBERJ5igBMReYoBTkTkKQY4EZGnivpFHmuDycswT4fSj09LHeqTnzidMZxciBqdrMsckQRqk24rP/ue2IDG493pTSP6F7ViyT2vOJ2xd+sXsWrknfTjfTXXobn9v53OAICR5HzMyvhl3vMC1CRPOZ0xmpyPRMaMMQGqHc+w2ECg7bHXcKB/8v17TWMduu5b790Mi+fKYtMIixml5s0VeBDemcX+83QIg8nLnM0IwjtzRo2OYTjprnCo74kNaDzWPWXTiMZj3eh7YoOzGUF4Z65j1cg72Lv1i85mAJPhnTlnlk4cdyUI78wZCZ047opF8X44WAHgQP8Q2h57zasZFs+VxaYRFjMseBPgQXhnCkLclSC8wzMyr8iL1Xi8O/sGBce7nc0Iwjs8I/OK3IUgvMNzZkWv15kmkWNGwuEMi+L9cLDmO16pMyyeK4tNIyxmWPAmwGODm0ZUnLhsIGDB4rmy2DQiLpt4MMCtcdOIihOXDQQsWDxXFptGxGUTD28C/LTUZS32Pz19F7fIRiSRdcaIJLL/DyLoX9SafYOCRa3OZuyruS7rjH011zmbAUz8wDLbnPMOc28sx4wxhzMsivevacz+Ps11vFJnWDxXFptGWMyw4E2A1yc/SYd48OH6LpTa5Il0iAcfru9CWXLPK+hf3Dpl04j+xW7vQmlu/+90iAcfpbgLpSZ5Kh3iwYfru1Cqk6fSIR58uL4LxaJ4v+u+9dOC1PUdIhYzLJ4ri00jLGZYKGpDh0JxQwciosLl2tDBmytwIiKaigFOROQpBjgRkacY4EREnmKAExF5igFOROQpBjgRkacY4EREniq6D1xEEgB6ABxW1ZuLP6XcRpMLkMj4xaMxEVQnT7odsve3wKuPAKcOAfOXATc+DDR/w+2Mn6wAzhyZfDz3UuD+/U5HWHSnA8BIciFmZbQ1npcEahz3p1utpdQs+qfj0mu++andeL138n20rmkhtt+11umMOHBxBf4vAN5z8O/5TEF4T+2FVowmF7gbsve3wB/uBU59DEAn/vzDvRPHXQmHNzDx+CcrnI2w6E4HJsN7ah/4GEYc9qdbraXULPqn49JrHg5vAHi99wQ2P7Xb2Yy4KCrARWQZgK8AeNrN6eQWhPfU+ZhyRV60Vx8BzofqJM+fnTjuSji88x2PwKI7HUA6vMNzZjnsT7daS6lZ9E/Hpdc8HN75jl/Iir0C/xmA7wIYz/UJInK3iPSISM/AwECR40rs1KHCjhPNkEX/NHvNLzyRA1xEbgbQr6pvftbnqeqTqtqiqi0NDQ1Rx9mYv6yw40QzZNE/zV7zC08xV+DrANwiIh8C+A2AL4nI807OKosxkRy90A7fnDc+DMwKfUHNmjNx3JW5lxZ2PAKL7nRg4geW2fvA3fWnW62l1Cz6p+PSa76uKfvPUHIdv5BFDnBVfVBVl6nqcgC3AfgvVd3i7MxCqpMn0yE+2Qvt+C6U5m8AX30cmH85AJn486uPu70L5f7908Pa8V0oFt3pAFCTPJEO8ck+cLd3oVitpdQs+qfj0mu+/a6108Kad6Fk56QPXETWA7g/322E7AMnIipcrj7wou8DBwBVfQ3Aay7+XURENDP8TUwiIk8xwImIPMUAJyLyFAOciMhTDHAiIk8xwImIPMUAJyLylJP7wK3sfvybuOH4S0hgHGOowhuLbsXae59zOqPviQ1oPN4NKAAB+he1Ysk9rzidcWrbatQP9aZnDNY1Yf4DbzmdMZ6cD8n4HS0VoCp5yukMADjSsRyX4NP046O4GJd2fuh0xrnkxajVyb60YanC7OSnn/G/KJxF/7RFj3Zzxy4MDk+2HtbXJrC3c6PTGRbrsGDRnV7qDnhvrsB3P/5NtB7/HaplHCJAtYyj9fjvsPvxbzqb0ffEBjQe64Yg1T0NoPFYN/qe2OBsxqltq1F/pnfKjPozvTi1bbWzGUF4Z3Zoi04cdykI78w5l+BTHOlY7mxGEN6ZM2p1HOeSFzubYdE/bdGjHQ5vABgcHkNzxy5nMyzWYcGiO92iA96bAL/h+EtZe6FvOP6SsxmNx7uzzmg83u1sRv1Qb9YZ9UO9zmYE4R2eIY5bRYPwDs/JvCIvVhDe4RmZV+TFsuiftujRDod3vuNRWKzDgkV3ukUHvDcBnshROZ7reCS5As5l8FnMIKLPZNGdbtEB702Aj+U41VzHI8nVTOuyTtliBhF9JovudIsOeG8C/I1Ft2bthX5j0a3OZvQvas06o39Rq7MZg3VNWWcM1jU5m6GCrDPU8f9JHMXFWecchbvvTw9LVdYZw+LurWvRP23Ro11fm72HPdfxKCzWYcGiO92iA96bAF9773PoXvQ1jOrEF/SoVqF70dec3oWy5J5X0L+4FYpU4AHoX+z2LpT5D7yFwblNU2YMznV7F0pV8lQ6xNMfJbgL5dLOD9MhHny4vgtldvLTdIgHH67vQrHon7bo0d7buXFaWLu+C8ViHRYsutMtOuCd9IHPFPvAiYgKl6sP3JsrcCIimooBTkTkKQY4EZGnGOBERJ5igBMReYoBTkTkKQY4EZGnGOBERJ4y/UUeERkA8JHZwOIsBnCs3CfhQFzWAcRnLVxHZfFhHVeqakP4oGmA+0REerL95pNv4rIOID5r4Toqi8/r4LdQiIg8xQAnIvIUAzy3J8t9Ao7EZR1AfNbCdVQWb9fB74ETEXmKV+BERJ5igBMReYoBnoWIJETkbRH5z3KfSzFEZIGIvCAi+0XkPRFxt8WMIRH5VxH5HxF5V0R2iMjscp/TTInIr0SkX0TezTi2UES6RORA6k93e9CVSI51bEu9t/aKyO9EZEE5z3Emsq0j4+/uFxEVkcXlOLcoGODZ/QuA98p9Eg78HMAuVV0B4B/g4ZpEZCmAewG0qOpKAAkAt5X3rAryHIDwnmbfB/Cqql4D4NXU40r3HKavowvASlVtBvB/AB60PqkInsP0dUBELgfQBuCg9QkVgwEeIiLLAHwFwNPlPpdiiEg9gC8AeAYAVHVEVU+W96wiqwYwR0SqAVwE4JMyn8+MqeqfAZwIHb4VwK9T//xrAJtMTyqCbOtQ1T+p6mjqYTeAZeYnVqAcrwcA/BTAdzGxTa03GODT/QwTL+R4uU+kSFcDGADwbOrbQU+LiF9bhwNQ1cMAfoKJK6MjAE6p6p/Ke1ZFW6KqRwAg9Wdjmc/HhW8B+GO5TyIKEbkFwGFV/Wu5z6VQDPAMInIzgH5VfbPc5+JANYDVAH6pqtcDGIIf/6k+Rer7w7cCuArAZQDqRGRLec+KMolIO4BRANvLfS6FEpGLALQDeLjc5xIFA3yqdQBuEZEPAfwGwJdE5PnynlJkhwAcUtU9qccvYCLQffNlAB+o6oCqngfwIoDPlfmcitUnIpcCQOrP/jKfT2QicgeAmwFsVj9/qaQJExcHf0193S8D8JaIXFLWs5ohBngGVX1QVZep6nJM/KDsv1TVy6s9VT0K4GMRuTZ16EYA/1vGU4rqIIBWEblIRAQT6/Duh7EhvwdwR+qf7wDwUhnPJTIR2QjgewBuUdW/l/t8olDVfaraqKrLU1/3hwCsTn39VDwGeLzdA2C7iOwFcB2AH5b5fAqW+i+IFwC8BWAfJt6z3vzqs4jsALAbwLUickhEvg3gxwDaROQAJu58+HE5z3EmcqzjFwDmAegSkXdE5N/KepIzkGMd3uKv0hMReYpX4EREnmKAExF5igFOROQpBjgRkacY4EREnmKAExF5igFOROSp/wdanWHQiBZVkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(b['Internal_Audit_Score'],b['External_Audit_Score'])\n",
    "plt.scatter(c['Internal_Audit_Score'],c['External_Audit_Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x211bc5614a8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(b['Internal_Audit_Score'],b['Fin_Score'])\n",
    "plt.scatter(c['Internal_Audit_Score'],c['Fin_Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x211bc5bc898>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(b['Internal_Audit_Score'],b['Past_Results'])\n",
    "plt.scatter(c['Internal_Audit_Score'],c['Past_Results'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x211bc61dc18>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(b['External_Audit_Score'],b['Fin_Score'])\n",
    "plt.scatter(c['External_Audit_Score'],c['Fin_Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x211bc68d0f0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(b['External_Audit_Score'],b['Loss_score'])\n",
    "plt.scatter(c['External_Audit_Score'],c['Loss_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x211bc6dcf28>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p7/7Sdthe2oneWkGAz+6rkg50uokCPCDp+xHx25J+Twn3ZHtA0lckDUbE5Zqe/nRLZ7tqyiOSrqtbu0PSsxFxqaRna7e73SOauY89ki6PiN+V9N+SNpXdVAse0cx9yPZySWslHSq7oXYQ4HVsXyjpekkPdrqXdtg+R9JnJD0kSRHxfkRMdLarli2S1Gt7kaQlmsfh2UWLiB9Iertu+SZJj9Y+f1TSUKlNtWC2fUTEMxHxQe3mi5oebN7VGjwekvS3kr4hKdVVHQT4TPdr+oH8sNONtOlTksYlfbd2OuhB22d1uqlmRcSYpG9p+sjoTUnvRMQzne2qbRdExJuSVPt4fof7KcIXJP1bp5tohe0bJY1FxI873UuzCPCT2L5B0tGI2NfpXgqwSNIVkr4dEaslvacc/1Q/Re388E2SLpa0TNJZttd3tiuczPadkj6QtKPTvTTL9hJJd0q6q9O9tIIAP9XVkm60/bqkxyV91vZjnW2pZYclHY6IvbXbT2g60LP5nKTXImI8Io5J2iXp0x3uqV1v2f6kJNU+Hu1wPy2zfbukGyTdFjlfVHKJpg8Oflz7vb9Q0ku2P9HRruaIAD9JRGyKiAsjYoWmnyh7LiJSHu1FxK8kvWF7ZW3pWkk/62BLrTokaY3tJbat6X2kezK2zpOSbq99fruk73Wwl5bZvk7SX0m6MSL+t9P9tCIiRiPi/IhYUfu9PyzpitrvT9cjwKvty5J22P6JpN+X9Dcd7qdptX9BPCHpJUmjmv6ZTfPSZ9s7Jb0gaaXtw7a/KGmrpLW2X9H0lQ9bO9njXDTYx99JOlvSHts/sv0PHW1yDhrsIy1eSg8ASXEEDgBJEeAAkBQBDgBJEeAAkBQBDgBJEeAAkBQBDgBJ/R8a7zYKlmBpyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(b['External_Audit_Score'],b['Past_Results'])\n",
    "plt.scatter(c['External_Audit_Score'],c['Past_Results'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x211bc74c278>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(b['Fin_Score'],b['Loss_score'])\n",
    "plt.scatter(c['Fin_Score'],c['Loss_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x211bc7ad128>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(b['Fin_Score'],b['Past_Results'])\n",
    "plt.scatter(c['Fin_Score'],c['Past_Results'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#With these figures some of the patterns are identified and subsetting the data:\n",
    "train_not_confirmed_result = train.loc[(train['Internal_Audit_Score']<9) & (train['External_Audit_Score']<9) &\n",
    "                                   (train['Fin_Score']<9) & (train['Loss_score']<9) & (train['Past_Results']<2),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Location_Score</th>\n",
       "      <th>Internal_Audit_Score</th>\n",
       "      <th>External_Audit_Score</th>\n",
       "      <th>Fin_Score</th>\n",
       "      <th>Loss_score</th>\n",
       "      <th>Past_Results</th>\n",
       "      <th>IsUnderRisk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>282.000000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>282.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.890071</td>\n",
       "      <td>41.635028</td>\n",
       "      <td>5.875887</td>\n",
       "      <td>5.514184</td>\n",
       "      <td>5.546099</td>\n",
       "      <td>5.429078</td>\n",
       "      <td>0.517730</td>\n",
       "      <td>0.280142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.235236</td>\n",
       "      <td>26.445357</td>\n",
       "      <td>1.692201</td>\n",
       "      <td>1.712881</td>\n",
       "      <td>1.699802</td>\n",
       "      <td>1.701038</td>\n",
       "      <td>0.500574</td>\n",
       "      <td>0.449867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.363000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>15.804750</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.500000</td>\n",
       "      <td>34.117000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>68.629500</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>80.809000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             City  Location_Score  Internal_Audit_Score  External_Audit_Score  \\\n",
       "count  282.000000      282.000000            282.000000            282.000000   \n",
       "mean    20.890071       41.635028              5.875887              5.514184   \n",
       "std     15.235236       26.445357              1.692201              1.712881   \n",
       "min      0.000000        5.363000              3.000000              3.000000   \n",
       "25%      6.000000       15.804750              5.000000              4.000000   \n",
       "50%     17.500000       34.117000              6.000000              5.000000   \n",
       "75%     38.000000       68.629500              7.000000              7.000000   \n",
       "max     44.000000       80.809000              8.000000              8.000000   \n",
       "\n",
       "        Fin_Score  Loss_score  Past_Results  IsUnderRisk  \n",
       "count  282.000000  282.000000    282.000000   282.000000  \n",
       "mean     5.546099    5.429078      0.517730     0.280142  \n",
       "std      1.699802    1.701038      0.500574     0.449867  \n",
       "min      3.000000    3.000000      0.000000     0.000000  \n",
       "25%      4.000000    4.000000      0.000000     0.000000  \n",
       "50%      5.000000    5.000000      1.000000     0.000000  \n",
       "75%      7.000000    7.000000      1.000000     1.000000  \n",
       "max      8.000000    8.000000      1.000000     1.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_not_confirmed_result.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remaining data is known to be sure Risk case:\n",
    "train_confirmed_result = train.loc[~train.index.isin(train_not_confirmed_result.index),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Location_Score</th>\n",
       "      <th>Internal_Audit_Score</th>\n",
       "      <th>External_Audit_Score</th>\n",
       "      <th>Fin_Score</th>\n",
       "      <th>Loss_score</th>\n",
       "      <th>Past_Results</th>\n",
       "      <th>IsUnderRisk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.157088</td>\n",
       "      <td>22.129299</td>\n",
       "      <td>10.689655</td>\n",
       "      <td>9.287356</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>5.639847</td>\n",
       "      <td>0.716475</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.039307</td>\n",
       "      <td>18.356635</td>\n",
       "      <td>2.773405</td>\n",
       "      <td>3.772088</td>\n",
       "      <td>3.546396</td>\n",
       "      <td>1.974984</td>\n",
       "      <td>0.962807</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.185000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>11.759000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.789000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>21.531000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>76.010000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             City  Location_Score  Internal_Audit_Score  External_Audit_Score  \\\n",
       "count  261.000000      261.000000            261.000000            261.000000   \n",
       "mean    18.157088       22.129299             10.689655              9.287356   \n",
       "std     14.039307       18.356635              2.773405              3.772088   \n",
       "min      0.000000        5.185000              3.000000              3.000000   \n",
       "25%      6.000000       11.759000             10.000000              6.000000   \n",
       "50%     13.000000       16.789000             11.000000             10.000000   \n",
       "75%     31.000000       21.531000             12.000000             12.000000   \n",
       "max     41.000000       76.010000             15.000000             15.000000   \n",
       "\n",
       "        Fin_Score  Loss_score  Past_Results  IsUnderRisk  \n",
       "count  261.000000  261.000000    261.000000        261.0  \n",
       "mean     8.666667    5.639847      0.716475          1.0  \n",
       "std      3.546396    1.974984      0.962807          0.0  \n",
       "min      3.000000    3.000000      0.000000          1.0  \n",
       "25%      6.000000    4.000000      0.000000          1.0  \n",
       "50%      8.000000    6.000000      1.000000          1.0  \n",
       "75%     11.000000    7.000000      1.000000          1.0  \n",
       "max     15.000000   13.000000     10.000000          1.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_confirmed_result.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "corr = train_not_confirmed_result.corr()\n",
    "sn.heatmap(corr, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    203\n",
       "1     79\n",
       "Name: IsUnderRisk, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_not_confirmed_result['IsUnderRisk'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.719858\n",
       "1    0.280142\n",
       "Name: IsUnderRisk, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_not_confirmed_result['IsUnderRisk'].value_counts([0]) #On seeing this the dataset is imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_not_confirmed_result.drop(['IsUnderRisk'],axis=1)\n",
    "Y = train_not_confirmed_result['IsUnderRisk']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
       "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier = RandomForestClassifier(n_estimators=1000, random_state=0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score,log_loss\n",
    "\n",
    "#print(confusion_matrix(y_test,y_pred))\n",
    "#print(classification_report(y_test,y_pred))\n",
    "#print(accuracy_score(y_test, y_pred))\n",
    "\n",
    "#Additionally, Grid search and CV is done on dataset, SVC and XGB models are generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv(r\"D:\\finance_default\\Test.csv\")\n",
    "test[\"index\"] = test.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rule which is identified from the data visualised pattern is implemented\n",
    "test_not_confirmed_result = test.loc[(test['Internal_Audit_Score']<9) & (test['External_Audit_Score']<9) &\n",
    "                                   (test['Fin_Score']<9) & (test['Loss_score']<9) & (test['Past_Results']<2),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_confirmed_result = test.loc[~test.index.isin(test_not_confirmed_result.index),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred_not_confirmed = classifier.predict_proba(test_not_confirmed_result.drop([\"index\"],axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.855, 0.145],\n",
       "       [0.351, 0.649]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred_not_confirmed[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred_not_confirmed = pd.DataFrame(test_pred_not_confirmed,columns=[\"0\",\"1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = test_not_confirmed_result\n",
    "df2 = test_pred_not_confirmed\n",
    "df1.reset_index(drop=True, inplace=True)\n",
    "df2.reset_index(drop=True, inplace=True)\n",
    "\n",
    "df = pd.concat( [df1, df2], axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ashik\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\ashik\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "#Rule which is identified from the data is implemented\n",
    "test_confirmed_result[\"0\"] = 0\n",
    "test_confirmed_result[\"1\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final_results = df.append(test_confirmed_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final_results = test_final_results.sort_values(by=['index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Location_Score</th>\n",
       "      <th>Internal_Audit_Score</th>\n",
       "      <th>External_Audit_Score</th>\n",
       "      <th>Fin_Score</th>\n",
       "      <th>Loss_score</th>\n",
       "      <th>Past_Results</th>\n",
       "      <th>index</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>18.272</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>64.799</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>68.890</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.855</td>\n",
       "      <td>0.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>16.492</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>17.178</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.351</td>\n",
       "      <td>0.649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   City  Location_Score  Internal_Audit_Score  External_Audit_Score  \\\n",
       "0    41          18.272                    13                    12   \n",
       "1    17          64.799                     6                    10   \n",
       "0    31          68.890                     3                     3   \n",
       "3     3          16.492                    15                    10   \n",
       "1     9          17.178                     7                     3   \n",
       "\n",
       "   Fin_Score  Loss_score  Past_Results  index      0      1  \n",
       "0          9           7             0      0  0.000  1.000  \n",
       "1          7           4             1      1  0.000  1.000  \n",
       "0          7           8             0      2  0.855  0.145  \n",
       "3          7           4             1      3  0.000  1.000  \n",
       "1          8           3             1      4  0.351  0.649  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_final_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_submission = test_final_results[[\"0\",\"1\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       0      1\n",
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     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "final_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_submission.to_excel(\"submission_21.xlsx\",index = False)"
   ]
  }
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